Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	37,246
2	Mississippi	34,547
3	Florida	33,802
4	North Dakota	33,314
5	Alabama	33,034
6	Arizona	30,693
7	Iowa	30,664
8	Tennessee	30,203
9	South Carolina	30,057
10	South Dakota	29,888
11	Arkansas	29,871
12	Georgia	29,341
13	Texas	28,300
14	Nevada	27,261
15	Idaho	26,215
16	Nebraska	25,878
17	Wisconsin	25,695
18	Utah	25,569
19	Illinois	24,836
20	Rhode Island	24,585
21	New York	24,408
22	Oklahoma	24,150
23	New Jersey	23,973
24	Missouri	23,255
25	Kansas	22,391
26	District of Columbia	22,337
27	Delaware	22,266
28	North Carolina	21,523
29	Maryland	21,489
30	California	21,446
31	Massachusetts	19,867
32	Indiana	19,611
33	Minnesota	19,145
34	Virginia	18,222
35	Kentucky	17,805
36	Connecticut	16,758
37	Puerto Rico	16,374
38	Montana	15,687
39	New Mexico	15,144
40	Michigan	14,674
41	Ohio	14,052
42	Alaska	13,558
43	Pennsylvania	13,511
44	Colorado	13,229
45	Washington	12,636
46	Wyoming	12,253
47	West Virginia	9,667
48	Hawaii	9,391
49	Oregon	8,564
50	New Hampshire	6,529
51	Maine	4,195
52	Vermont	2,945

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Dakota	694
2	North Dakota	666
3	Montana	511
4	Wisconsin	448
5	Idaho	376
6	Iowa	368
7	Utah	337
8	Nebraska	310
9	Oklahoma	301
10	Arkansas	299
11	Missouri	295
12	Tennessee	276
13	Wyoming	266
14	Mississippi	236
15	Louisiana	230
16	Kentucky	206
17	Rhode Island	196
18	Illinois	191
19	Alaska	185
20	Indiana	185
21	Minnesota	184
22	North Carolina	180
23	New Mexico	178
24	Kansas	168
25	South Carolina	168
26	Texas	164
27	Nevada	154
28	Alabama	153
29	Florida	126
30	Georgia	126
31	Michigan	126
32	Colorado	124
33	Ohio	122
34	Virginia	116
35	Delaware	109
36	West Virginia	108
37	Arizona	106
38	Puerto Rico	103
39	District of Columbia	102
40	Pennsylvania	100
41	New Jersey	91
42	Maryland	90
43	Washington	88
44	Oregon	84
45	California	81
46	New York	78
47	Massachusetts	71
48	Hawaii	68
49	Connecticut	58
50	New Hampshire	48
51	Maine	23
52	Vermont	11

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,819
2	New York	1,689
3	Massachusetts	1,387
4	Connecticut	1,269
5	Louisiana	1,206
6	Rhode Island	1,063
7	Mississippi	1,032
8	District of Columbia	898
9	Arizona	789
10	Illinois	723
11	Michigan	720
12	Florida	701
13	South Carolina	682
14	Georgia	671
15	Delaware	668
16	Maryland	658
17	Pennsylvania	653
18	Texas	581
19	Indiana	555
20	Alabama	537
21	Nevada	535
22	Arkansas	498
23	Iowa	454
24	New Mexico	428
25	Ohio	426
26	California	415
27	North Dakota	412
28	Tennessee	391
29	Virginia	389
30	Missouri	384
31	Minnesota	383
32	Colorado	365
33	North Carolina	356
34	New Hampshire	329
35	South Dakota	307
36	Washington	299
37	Kentucky	284
38	Idaho	282
39	Oklahoma	274
40	Nebraska	267
41	Kansas	253
42	Wisconsin	246
43	Puerto Rico	223
44	West Virginia	206
45	Montana	184
46	Utah	157
47	Oregon	141
48	Hawaii	115
49	Maine	105
50	Wyoming	93
51	Vermont	92
52	Alaska	76

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	14
2	South Dakota	9
3	Missouri	7
4	Arkansas	6
5	Mississippi	6
6	Alabama	5
7	Florida	5
8	Tennessee	5
9	Georgia	3
10	Idaho	3
11	Indiana	3
12	Iowa	3
13	Kansas	3
14	South Carolina	3
15	Texas	3
16	California	2
17	Delaware	2
18	Illinois	2
19	Montana	2
20	Nevada	2
21	North Carolina	2
22	Oklahoma	2
23	Puerto Rico	2
24	Utah	2
25	Virginia	2
26	Wisconsin	2
27	Arizona	1
28	Colorado	1
29	District of Columbia	1
30	Hawaii	1
31	Kentucky	1
32	Louisiana	1
33	Massachusetts	1
34	Michigan	1
35	Minnesota	1
36	Nebraska	1
37	Ohio	1
38	Oregon	1
39	Pennsylvania	1
40	Rhode Island	1
41	Washington	1
42	West Virginia	1
43	Alaska	0
44	Connecticut	0
45	Maine	0
46	Maryland	0
47	New Hampshire	0
48	New Jersey	0
49	New Mexico	0
50	New York	0
51	Vermont	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	161,241	1	99
Chattahoochee	Georgia	155,588	2	99
Trousdale	Tennessee	150,833	3	99
Lafayette	Florida	147,708	4	99
Lake	Tennessee	134,122	5	99
Davidson	Tennessee	43,096	214	93
Richland	South Carolina	38,371	307	90
York	South Carolina	20,578	1260	59
Orange	California	17,886	1517	51
Pierce	Washington	10,479	2312	26

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	5,085	1	99
Kenedy	Texas	4,950	2	99
Emporia city	Virginia	4,863	3	99
Galax city	Virginia	4,727	4	99
Randolph	Georgia	4,279	5	99
Richland	South Carolina	604	746	76
Davidson	Tennessee	460	1048	66
Orange	California	411	1159	63
York	South Carolina	303	1453	53
Pierce	Washington	254	1617	48

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.